Binary Networks and Continual Learning for Pose Estimation from a Single Aerial Image
TL;DR
This work proposes a methodology using a binary network with a Continual Learning (CL) strategy to create an estimation model to create an estimation model during the same flight mission for Pose estimation using aerial images captured by UAVs.
Abstract
Pose estimation using aerial images captured by Unmanned Aerial Vehicles (UAVs) allows the localisation in GPS-denied scenarios. Several methods based on deep learning approaches with convolutional neural networks (CNN) have become tools for estimating localisation from images. However, building a model that can estimate the pose from a single image needs a large dataset and training time to obtain a result. Besides, the model can be inappropriate in assessing the correct pose in dynamic scenarios with multiple changes. Therefore, we propose a methodology using a binary network with a Continual Learning (CL) strategy to create an estimation model during the same flight mission. Also, we use a submap scheme and multiple models to acquire the UAV’s localisation into different parts of the trajectory. Finally, we use PoseNet, ORB-SLAM2 and single-model for comparison purposes in four scenarios, achieving a percentage error of 14% of the total trajectory and a processing time of 51 ms with our proposed approach.